ArticleLa Radiologia medica2022
Automatic PI-RADS assignment by means of formal methods.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 25 citations in OpenAlex.
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- Computed Tomography Urography: State of the Art and Beyond.Tomography (Ann Arbor, Mich.) · 2023Review
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- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Dose Reduction Strategies for Pregnant Women in Emergency Settings.Journal of clinical medicine · 2023Review
- Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications.Journal of clinical medicine · 2023Review
- Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence.Biology · 2023Review
- Immunotherapy Assessment: A New Paradigm for Radiologists.Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.Current oncology (Toronto, Ont.) · 2023Article
- Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence.Cancers · 2023Review
- A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls.Diagnostics (Basel, Switzerland) · 2022Review
- Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases.La Radiologia medica · 2022Article
- Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases.La Radiologia medica · 2022Article
- Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern.Diagnostics (Basel, Switzerland) · 2022Article
- Combined Hepatocellular-Cholangiocarcinoma: What the Multidisciplinary Team Should Know.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
objectivesThe Prostate Imaging Reporting and Data System (PI-RADS) version 2 emerged as standard in prostate magnetic resonance imaging examination. The Pi-RADS scores are assigned by radiologists and indicate the likelihood of a clinically significant cancer. The aim of this paper is to propose a methodology to automatically mark a magnetic resonance imaging with its related PI-RADS. MATERIALS AND
methodsWe collected a dataset from two different institutions composed by DWI ADC MRI for 91 patients marked by expert radiologists with different PI-RADS score. A formal model is generated starting from a prostate magnetic resonance imaging, and a set of properties related to the different PI-RADS scores are formulated with the help of expert radiologists and pathologists.
resultsOur methodology relies on the adoption of formal methods and radiomic features, and in the experimental analysis, we obtain a specificity and sensitivity equal to 1. Q
conclusionsThe proposed methodology is able to assign the PI-RADS score by analyzing prostate magnetic resonance imaging with a very high accuracy.
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